HO CHI MINH UNIVERSITY OF BANKINGPHAM LE QUYNH HOA FACTORS AFFECTING THE CUSTOMER’S PURCHASE INTENTION IN THE PAID MUSIC STREAMING SERVICES IN HO CHI MINH CITY GRADUATION THESIS Majo
INTRODUCTION
Background of the study
According to a Q&Me report that conducted a survey based on 1500 people including
In 2021, music was the most popular form of entertainment among Vietnamese people aged 18 to 44, with over 70% listening to music for more than 30 minutes daily Vietnamese users primarily favor free music streaming platforms like YouTube Music, Zing MP3, and NhacCuaTui, due to the lack of comprehensive copyright protection laws for music and movies in Vietnam Recently, the arrival of international music services such as Spotify, Apple Music, and iTunes has significantly transformed the Vietnamese music market landscape, offering more diverse and legal options for music enthusiasts.
Streaming has become a dominant force in developed music industries, accounting for 65% of the global music industry’s revenue in 2022, with over 532 million paid subscription users, according to the International Federation of Phonographic Recordings (IFPI) This rapid growth reflects how streaming fosters a civilized music environment by aligning the interests of artists, listeners, and copyright protection In Vietnam, streaming is still in its early stages but shows significant potential, with 39.6% of people listening to streaming services and an average daily listening time of about 1 hour and 11 minutes (as of January 2022, We Are Social) The music streaming market is projected to reach US$26.70 million in revenue in 2022, with an expected compound annual growth rate (CAGR) of 6.66% from 2022 to 2027, potentially reaching US$36.85 million by 2027 Success in this competitive landscape hinges not only on copyright and market monopoly but also on technological innovations aimed at enhancing the user listening experience.
Research on music streaming services has garnered significant attention from both domestic and international scholars, highlighting various factors influencing consumers’ willingness to purchase and use paid music platforms Drr et al (2010) explored how different service features impact users’ willingness to pay for MaaS by surveying 132 users, while Drr (2012) applied the Theory of Planned Behavior to analyze the motivations behind music piracy and paid service usage, revealing that users' attitudes and subjective norms strongly predict their intention to adopt MaaS Additionally, the perceived advantages of MaaS over illegal downloads positively influence user attitudes Conversely, Wagner, Benlian, and Hess (2013) found that within freemium models, free and premium versions do not function as advertising tools for each other, indicating no promotional effect between the two tiers These studies collectively shed light on consumer behavior and the effectiveness of different monetization strategies in the music streaming industry.
Research has shown that users' willingness to pay for paid MaaS and music streaming services exists despite the availability of free alternatives Aapeli (2016), using the UTAUT2 model, emphasizes that paid MaaS services should focus on providing good price value and hedonic pleasure while leveraging users' habitual system usage Similarly, Mariana and Pedro (2021) confirm that habit, performance expectancy, and price value are key factors influencing the intention to subscribe to paid music streaming services Additionally, emerging dimensions such as personalization, attitudes toward piracy, and perceived fit between freemium and premium models are increasingly relevant in driving adoption of these services.
This thesis utilizes the Theory of Planned Behavior (TPB) as its fundamental theoretical framework, highlighting its status as one of the most influential predictive persuasion theories The TPB has been extensively applied in the Information Systems field to assess user acceptance and adoption, providing valuable insights into behavioral intentions Its robust predictive capabilities make it a suitable choice for developing a comprehensive conceptual model in this study, enabling a better understanding of factors influencing user behavior.
The Theory of Planned Behavior (TPB) is valued for its flexibility in incorporating new variables, making it widely applicable in studies on music distribution channel acceptance (Ajzen, 1991; d‘Astous et al., 2005; Plowman & Goode, 2009) According to Ajzen (1991), actual behavior is influenced by three key factors: attitudes towards behavioral outcomes, subjective norms, and perceived behavioral control This study extends the TPB framework by examining how age, gender, and occupation moderate the intention to use paid Music Streaming Services (MSS) among consumers in Ho Chi Minh City While prior research in other countries has applied TPB to understand MSS adoption, cultural, economic, and developmental differences highlight the need for context-specific studies Despite the rapid growth of the music industry and the focus on music app development, the proportion of paying users remains modest, and research from a customer perspective in Vietnam, particularly Ho Chi Minh City, is limited.
Studying the factors influencing customers’ purchase decisions for paid music streaming services in Ho Chi Minh City is crucial for developing targeted marketing strategies The insights gained from this research can enable companies to tailor solutions to specific customer segments, enhancing their competitive advantage To address this, the study employs an adjusted and supplemented Theory of Planned Behavior (TPB) model adapted to the Vietnamese context, aiming to identify which factors significantly impact customers’ decisions to pay for music streaming services and their application usage behavior Understanding the degree of influence of each factor on the willingness to pay is essential for optimizing service offerings and increasing user conversion rates.
Customer’s Purchase Intention In The Paid Music Streaming Services In Ho Chi Minh City.
Research Objectives and Research Questions
The study suggests several approaches to innovate and increase quality in order to develop a business growth plan
Identify the factors of paid music streaming services affect on the customer‘s purchase intention in the paid music streaming services in Ho Chi Minh City
Determine the level impact of each factor on consumer purchase decisions
This study explores the key factors influencing consumer purchasing intentions for paid music streaming services in Ho Chi Minh City It aims to identify how elements such as service quality, pricing strategies, user experience, and brand reputation impact customers' decisions to subscribe The research seeks to provide insights into the determinants that drive consumers toward paid music streaming platforms in this vibrant Vietnamese market.
Which factors of paid music streaming services affect to the intentions to purchase services of customers in Ho Chi Minh City?
To what extent each factor affect customers‘ intentions to purchase services?
1.3 Research Subjects and The Scope of Research
A study of investigating of factors of paid music streaming services which have significant impact on customers‘ intentions to purchase services in Ho Chi Minh City
In term of space: customers in Ho Chi Minh City
In term of time: from August to November, 2022
This study was carried out using a combination of qualitative and quantitative research methods
The qualitative research method involves reviewing and synthesizing existing scholarly works both domestically and internationally to clarify key research concepts and develop effective questionnaires This process is complemented by in-depth individual interviews, which are utilized to explore and construct observed variables for measuring the research concepts and to establish robust theoretical models Data collection is conducted through surveys of selected subjects, following a predefined outline to ensure consistency and accuracy in gathering relevant information.
This study employs a quantitative research method, utilizing techniques such as Cronbach's Alpha reliability test, exploratory factor analysis (EFA), and structural equation modeling (SEM) to analyze data Data was collected through a consumer questionnaire in Ho Chi Minh City, with an estimated sample size of 300 respondents The quantitative results serve to validate insights obtained from previous qualitative research, enabling a comprehensive evaluation and conclusion of the research problem.
1.5 The practical significance of the thesis
This study integrates the Theory of Planned Behavior (Ajzen, 1985, 1991) with the Perceived Value and Perceived Risk theories to identify new observed variables previously unexplored in Vietnam's online music service purchase research The research aims to determine key factors influencing consumers' intentions to buy paid online music services, with a particular focus on psychological aspects and perceived risks associated with product usage The findings provide valuable insights for businesses to develop effective strategies for promoting online music subscriptions and enhancing customer conversion efforts.
In addition to the introduction, conclusion, list of references, the thesis content is structured into 5 chapters:
Chapter 5: Conclusion and suggesting solutions
Chapter 1 introduces the research topic by outlining the reasons for its selection, clearly defining the research objectives and scope, and detailing the research methods and overall structure of the study This foundational chapter sets the stage for the subsequent sections, guiding the reader through the purpose and framework of the research.
LITERATURE REVIEW
Previous Empirical Studies
2.1.1 The model of Vafa Saboori-Deilami and Chang Seob Yeo (2019)
The study "Paid Music Streaming: What Drives Customers’ Choice?" by Saboori-Deilami and Yeo (2019) explores the key factors influencing consumer decisions when choosing online music streaming services, emphasizing both subjective elements like customer perception and objective characteristics of the service It highlights that perceived value mediates the relationship between service quality and the likelihood of subscription, suggesting that balancing quality and perceived value is essential for attracting customers While high service quality is attractive, consumers prioritize overall value, making it critical for providers to find an optimal balance However, the research’s limitations include focusing only on the top three streaming services—Apple Music, Spotify, and Pandora—which, while dominating over half the market, may not fully represent the industry Additionally, relying solely on primary data from a large sample restricts the scope, indicating the need for broader analysis to strengthen conclusions.
For enhanced validity, future studies should incorporate multiple data collection methods, such as secondary information and qualitative data, alongside primary data This mixed-methods approach can help corroborate findings and strengthen the overall reliability of the research.
Figure 1 - Proposed Conceptual Model by Vafa Saboori-Deilami and Chang Seob Yeo (2019)
2.1.2 The model of Chai et al (2022)
In ―Digital Music: A Study Of Factors In Influencing Online Music Streaming Service Purchase Intention‖ by J Y Chai, L K K Ken, K H Chan, S X Wan1 and
T T Ting (2022) investigates the factors influencing consumers' willingness to subscribe to online music streaming services, emphasizing purchase intention The study collected data through an online questionnaire using Google Forms, adapted from Barros' 2017 research, with 200 Malaysian respondents Data analysis involved Pearson Correlation and Cohen’s f2 to assess effect sizes and hypothesis testing The findings reveal that key factors such as Perceived Value, Tangibility Preference, Music Affinity, and Music Piracy Awareness significantly impact consumers' subscription intentions.
10 factors that will significantly influence the consumers‘ Purchase Intention in online music streaming service
This study utilized an online questionnaire, which offers the advantage of significantly reducing data collection time However, there are limitations to this research approach that should be acknowledged.
This data collection method primarily targets Internet users, most of whom are students, which may result in a limited and biased sample Consequently, the study's findings might not accurately represent the entire population To enhance the validity and generalizability of the results, future research should include respondents from diverse age groups and ethnic backgrounds, providing a more comprehensive perspective.
This research primarily focuses on consumer purchase intention towards Online Music Streaming Services (OMSS), but does not examine the specific platforms used by respondents, which could provide insights into platform strengths and weaknesses Future studies should include platform selection to better understand its impact on purchase intention Additionally, the current study measures only declared purchase intentions and does not consider all potential influencing factors, due to the complexity of including numerous variables Future research should explore additional factors such as fashion consciousness, economic status, age, and gender to gain a comprehensive understanding Moreover, promising avenues for further investigation include the influence of advertising on consumer decisions and the factors driving in-app purchases within OMSS.
Figure 2 - Conceptual Model by J Y Chai, L K K Ken, K H Chan, S X Wan1 and T T Ting
2.1.3 The model of Mariana and Pedro (2021)
This study explores the key factors influencing music streaming consumption, focusing on the intention to adopt premium paid plans and recommend services An extended UTAUT2 model was developed and tested using data from 324 users through structural equation modeling (SEM) Findings reveal that habit, performance expectancy, and price value are primary drivers of users’ willingness to subscribe to paid music streaming services Additionally, new factors such as personalization, attitudes towards piracy, and perceived fit between freemium and premium models significantly impact consumer adoption The research offers valuable insights into consumer behaviors in music streaming, providing practical and theoretical implications for service providers aiming to enhance user engagement and loyalty.
Figure 3 - Conceptual Model by Mariana Lopes Barata and Pedro Simoes Coelho (2021)
2.1.4 The model of Thomas M Wagner and Thomas Hess (2013)
The article "What Drives Users to Pay for Freemium Services? Examining People’s Willingness to Pay for Music Services" by Thomas M Wagner and Thomas Hess (2013) presents a research model based on the Theory of Planned Behavior to explore factors influencing consumers' willingness to pay for premium music services, despite the availability of free alternatives The study’s findings, derived from a survey of 157 participants, reveal that utilizing the free version negatively affects users’ intentions to upgrade to the paid premium service Notably, this research is the first to examine users' willingness to pay for a premium service in the context of a free basic option The study contributes to the theoretical understanding of consumer behavior in freemium models, reaffirming the importance of perceived value and intentions in subscription decisions.
TPB‘s applicability to new research contexts Overall, we were able to explain 49.6% of the variance in people‘s intentions to use premium MaaS
The desire to use free MaaS services strongly discourages users from paying for premium options, as many are satisfied with the free version and see no need for upgrade To encourage conversion, MaaS providers should offer a free trial of the full premium service, allowing users to experience its benefits before a time-limited trial ends and payment becomes necessary This approach contrasts with traditional feature-limited freemiums and promotes a full experience upfront Additionally, user attitude and subjective norms—such as social influences from family and friends—significantly impact willingness to pay; leveraging social media marketing, like sponsored links and social sharing features (e.g., Spotify’s playlist sharing via Facebook), can enhance perceived value and increase adoption among potential users.
This study's limitations include a sample consisting solely of students, which may not represent the broader MaaS user base, and the focus on MaaS as an example, limiting the generalizability to other freemium services While various internet industries employ different strategies to incentivize paid upgrades—such as offering higher quality content or time advantages—our model did not account for these specific techniques Therefore, its applicability across industries remains untested Importantly, our findings suggest that clearly distinguishing between free and premium products can enhance users' willingness to subscribe to the paid version.
Lock-in effects from the free version can positively influence users’ willingness to pay, highlighting the importance of understanding habitual usage patterns Future research should explore the relationship between user habits and lock-in effects in greater detail to optimize monetization strategies.
Figure 4 - Conceptual Model of Thomas M Wagner and Thomas Hess (2013)
Theoretical Background and Hypothesis Development
2.2.1.1 The Theory of Planned Behavior
The Theory of Planned Behavior (Ajzen, 1985, 1991) suggests that human behavior is primarily shaped by behavioral intentions, which are influenced by individuals' attitudes toward the specific behavior According to Ajzen, attitude reflects the degree to which a person evaluates the behavior as favorable or unfavorable, based on their beliefs about the expected outcomes and the perceived appropriateness of those outcomes In addition to attitude, subjective norms and perceived behavioral control also play crucial roles in shaping behavioral intentions Understanding these factors is essential for accurately predicting and influencing human behavior across various contexts.
15 perceived consequences for the individual and the level of desirability for those consequences
Subjective norm, as defined by Ajzen (1991), refers to the perceived social pressure to perform or not perform a specific behavior It reflects an individual's mindset about whether significant others believe the behavior should be undertaken The influence of subjective norm increases with the individual's willingness to comply with the opinions of important referents Ultimately, the total subjective norm is determined by the individual's perception of the referent’s judgment and their willingness to conform to those perceived beliefs.
Perceived behavioral control is a crucial element in the Theory of Planned Behavior, distinguishing it from the Theory of Reasoned Action According to Ajzen (1991), this concept plays a pivotal role in predicting behavioral intentions and actual behaviors Fishbein and Ajzen (1980) originally developed the Theory of Reasoned Action, but it was Ajzen's later work that emphasized perceived behavioral control as a key factor in understanding human actions.
According to Fishbein and Ajzen (1980), intentions are primarily influenced by subjective norms and attitudes toward the behavior Ajzen (1991, p 183) further explains perceived behavioral control as individuals’ perception of how easy or difficult it is to perform a specific behavior These concepts are fundamental in understanding how psychological factors shape behavioral intentions and actions.
According to this theory, individuals perceive varying levels of control over their behaviors, ranging from easy-to-perform actions to those requiring significant effort and resources Ajzen posits that there is a connection between perceived behavioral control and actual behavior, though measuring true control is challenging Therefore, perceived behavioral control serves as an effective proxy for assessing actual control, facilitating a better understanding of behavioral intentions and actions.
The Technology Acceptance Model (Davis, 1989) is a highly regarded and empirically validated framework for understanding user behavior across various computer system applications This influential model has been extensively used in research to analyze factors that influence technology adoption, highlighting its significance in the field of information systems (Davis et al., 1989; Mathieson, 1991; Szajna, 1996).
According to the Technology Acceptance Model (Davis, 1989), users’ attitudes toward a computer system significantly influence their intention to use it, ultimately affecting actual system usage Hu et al (1999) and Koufaris (2002) support this view, emphasizing that when users encounter new technology, their perceptions are shaped by two key factors that influence their attitude and subsequent decision-making regarding technology adoption.
According to Davis (1989) these two factors are ―perceived ease of use‖ and
Perceived usefulness of new technology refers to the extent to which users believe that using a system will enhance their job performance Davis (1989) defines perceived ease of use as the degree to which individuals believe that operating a system would require minimal effort, promoting user acceptance and successful adoption.
The Technology Acceptance Model (TAM) has been extensively revised by scholars, with Venkatesh and Davis (2000), Venkatesh and Bala (2008), and Venkatesh et al (2003) contributing significant updates Venkatesh and Davis (2000) provided a detailed explanation of users' perceived usefulness and ease of use, analyzing these perceptions across three time frames: before implementation, after implementation, and long after implementation They argued that users' perceptions of a computer system's usefulness are primarily based on their mental judgment of how well the system aligns with their work objectives and the results obtained when performing job-related tasks Their findings demonstrated that these perceptions are valid in both voluntary and mandatory work environments, highlighting the importance of aligning technology features with user objectives for successful acceptance.
Since customers of online music streaming services are in effect computer users, therefore, their behaviors, decisions, and reactions towards this new disruptive
17 technology could be well explained by a behavioral information system theory like technology acceptance model
Perceived value is the overall utility a consumer perceives from a product or service based on a cost-benefit assessment (Zeithaml, 1988), and its success depends on specific consumer-desired values related to functional, product, or technical aspects Numerous studies have measured these values from the consumer’s perspective, particularly for technologies and services like streaming platforms (Praveena & Thomas, 2014; Pal & Triyason, 2017) Understanding the impact of these perceived values on technology adoption provides valuable insights for consumers The concept of perceived value has been extensively examined in relation to information systems, mobile services, streaming, and brand behavior, with theories ranging from unidimensional price-based value (Marchand & Hennig-Thurau, 2013) to multidimensional frameworks such as consumption and means-end theories (Sheth et al., 1991) Originally, Sheth et al identified five broad aspects of perceived value—social, emotional, conditional, epistemic, and functional—later refining functional value into monetary and convenience categories to better suit online services These aspects are particularly relevant as they capture the experiential and cognitive dimensions of value derived from repeated use of online platforms (Gummerus, 2013; Chen et al., 2017; Ali, 2018).
Perceived value theory is highly relevant to streaming services due to their personalized and experience-based offerings, providing viewers with tailored content that enhances their overall experience (Oyedele & Simpson, 2018) Key dimensions such as convenience value, enabling ease, speed, and accessibility across multiple devices, and monetary value, comparing costs with traditional media like DTH or TVs, are crucial in shaping viewer perceptions (Sheth et al., 1991; Praveena & Thomas, 2014) Emotional value, reflected in perceived enjoyment and fun, plays a significant role in user engagement within streaming platforms (Davis et al., 1992; Chang et al., 2017) While social value may not directly influence usage, it can impact viewers' social identity as enthusiasts of music or videos (Oyedele & Simpson, 2018) In the Indian context, where consumers tend to compare perceived benefits across products and services, the application of perceived value theory is particularly pertinent, yet research has largely focused on other aspects, with limited studies exploring the multidimensional aspects of value and their influence on continued streaming service usage (Singh et al., 2020; Borja et al., 2015; Lee et al., 2016; Chen et al., 2017; Pal & Triyason, 2017; Cai et al., 2018; Yang & Lee, 2018).
Perceived risk, as defined by Schierz et al (2010), is the expectation of potential losses, with higher anticipated losses leading to a greater perception of risk among consumers Laroche et al (2005) further elaborated that perceived risk involves negative perceptions of unpredictable and variable outcomes resulting from a purchase Understanding these facets of perceived risk is crucial for businesses aiming to address consumer concerns and improve trust.
Perceived risk, as defined by Ko et al (2004), refers to consumers' perception of potential unfavorable outcomes associated with purchasing a product or service This concept encompasses two key elements: indecisions, which relate to the likelihood of negative results, and consequences, which denote the significance of potential losses (Laroche et al., 2005) Additionally, Kim et al (2003) noted that consumers’ beliefs about uncertain outcomes are particularly influenced by their online shopping experiences.
Perceived risk plays a crucial role in shaping consumer purchase intentions, significantly impacting their evaluations and buying behaviors (Ko et al., 2004) Consumers typically perceive higher risks when shopping online compared to physical stores, which can deter online purchases Lee and Tan (2003) highlight that consumers with elevated perceived risks are less likely to buy products or services online Overall, perceived risks negatively influence consumers' willingness to make online purchases, emphasizing the importance of addressing perceived risk factors to boost e-commerce sales (Liu and Wei, 2003).
Research by Kim and Lennon (2013) shows that higher perceived risks associated with online shopping diminish consumers' purchase intentions Similarly, Akhlaq and Ahmed (2015) found that perceived risk negatively impacts consumers’ willingness to buy online, leading to decreased purchase intentions when consumers perceive transactions as risky This trend is supported by Zhao et al (2017), who confirm that perceived risk consistently hinders online apparel purchases Overall, these studies demonstrate that perceived risk is a significant barrier to online shopping, reducing consumers’ likelihood to buy from e-retailers.
Thus, it also verified that perceived risk plays a negative role in online purchase intentions
METHODOLOGY
Research Process
The study uses qualitative and quantitative research methods, including:
This research aims to identify, refine, and enhance the observed variables utilized to measure key research concepts Following the outlined steps and interpretations depicted in Figure 3.1, the study ensures accurate measurement and validity of the research constructs, aligning with best practices in research methodology and data analysis.
Step 1: Research topic and background of the topic
Step 2: Research Previous expirical studies & Theoretical basis
After developing the theoretical framework, the author refines the model by incorporating relevant observed variables to accurately measure each survey factor A formal questionnaire is then created to assess the key variables, including Intrinsic Rewards (IR), Extrinsic Rewards (ER), Identity Salience (IS), Psychological Ownership (PO), and various risks such as Finance Risk (FR), Time Risk (TR), Product Risk (PR), and Security Risk (SR).
Quantitative research is conducted after qualitative research to refine observed variables based on initial findings A structured questionnaire is then developed to officially survey customers in Ho Chi Minh City through face-to-face interviews or email, gathering data from 251 respondents The collected data is analyzed using SPSS 22.0 software to generate statistical results that inform subsequent research steps, as illustrated in Figure 3.1.
After collecting customer survey data, the researcher meticulously entered, filtered out inappropriate questionnaires, cleaned the dataset, and verified the normal distribution of the data These steps ensured the accuracy and quality of the data, allowing for a reliable analysis of the survey results.
32 of Cronbach's Alpha of the scale to eliminate inappropriate observations Reliability test results have two cases:
• Case 1: The scales do not reach the appropriate reliability, then go back to step 1 to proceed from the beginning
• Case 2: The scales achieve the appropriate reliability, then proceed to the next step, which is the exploratory factor test
In Step 5, perform an exploratory factor analysis (EFA) on both independent and dependent variables to assess convergence among observations This process helps identify the most representative factors within each group of observed variables Selecting these key factors ensures accurate data reduction and enhances the validity of subsequent analyses Utilizing the identified representative factors allows for more precise and meaningful results in the following research steps.
In Step 6, the author selects representative variables identified through the EFA factor test to run the regression analysis The results of this regression model are then thoroughly discussed to interpret the relationships between variables Additionally, the author performs diagnostic tests to identify potential model issues, including multicollinearity, autocorrelation, and heteroscedasticity, ensuring the robustness and validity of the regression analysis.
Step 7: From the results of the regression model, author will discuss and compare these results with previous research, from which there are suggestions for policy implications.
Survey Design
The scale was recalibrated based on outcomes from group discussions during the preliminary study, incorporating expert suggestions to rebuild the eight factor groups To assess the observed variables, a 7-point Likert scale was employed, ranging from strongly disagree (1) to strongly agree (7) This scale enables precise measurement of respondents' attitudes and perceptions, with 1 indicating strong disagreement and 7 indicating strong agreement.
Table 3.1: Summary of scales of factors in the research model
(1) I used Music Streaming Services (MSS) because I found it enjoyable IR1
Venkatesh et al 2012; Chu and Lu 2007
(2) I used Music Streaming Services (MSS) because
Music Streaming Services (MSS) is pleasant IR2
(3) I used Music Streaming Services (MSS) because
Music Streaming Services (MSS) makes me happy IR3
(4) I used Music Streaming Services (MSS) because
Music Streaming Services (MSS) is exciting IR4
(6) Sound quality is the reason I used MSS ER1
Venkatesh et al 2012; Chu and Lu 2007
(7) Ultimate features are the reason why I used MSS ER2
(8) I used MSS because I wanted to save time ER3
(9) I can acquire music information more easily through paid music streaming services ER4
(10) Using paid MSS is an important part of who I am IS1
(11) It is considered prestigious in my community to use paid MSS IS2
(12) Using paid MSS improves my credibility among social acquaintances IS3
(13) People in my community are proud to use paid MSS IS4
(14) I sense that paid MSS is mine PO1 Brown et al
(15) I feel a very high degree of personal ownership for paid MSS PO2
(16) I feel a very high degree of personal ownership for the paid MSS that I bought PO3
(17) The paid MSS I bought is MINE PO4
(18) I tend to over spend for paid music Streaming
(19) I might get overcharged for paid music Streaming
(20) Paid music Streaming Services may not be worth the money I spent FR3
(21) Paid MSS can waste my time TR1
(22) I have a hard time finding paid MSS that works for me TR2
(23) I do not trust the online company providing Paid
MSS PR1 Ariffin et al
(24) I am unable to find the desired product of Paid MSS PR2
(25) I might not get the right quality for my money for
(26) I feel that my Paid MSS account information is insecure SR1
(27) The website may be insecure SR2
(28) Companies that provide Paid MSS may disclose my SR3
No Items Code Reference information
(29) Information about the company providing the Paid
MSS may not be sufficient SR4
Usage Continuance Intention toward paid MSS (Int)
(34) I intend to continue to use paid MSS Int1 Venkatesh et al 2012
(35) I think I am going to use paid MSS within six months Int2
(36) I think I am going to use paid MSS within one year Int3
Data Analysis Methods
Preliminary assessment of the reliability of the scale
The reliability of research scales is evaluated using Cronbach's Alpha coefficient, which assesses the internal consistency of the scale by measuring how closely the items in the scale correlate with each other According to researchers like Hoang Trong, Chu Nguyen Mong Ngoc, Peterson (1994), and Slater (1995), a Cronbach's Alpha of 0.8 or higher (close to 1) indicates a high-quality measurement scale, while values from 0.7 to nearly 0.8 are considered acceptable Additionally, some researchers suggest that a Cronbach's Alpha of 0.6 or higher can be appropriate, especially when the scale measures a new or unfamiliar concept to the user.
Cronbach's Alpha coefficient measures the reliability of a scale, but determining which observed variables to retain or remove requires examining the item-total correlation coefficient Variables with a correlation coefficient less than 0.3 are typically considered for removal However, Nguyen Dinh Tho and Nguyen Thi Mai Trang (2008) emphasize that the decision to include or exclude variables should not be based solely on statistical criteria but also on the content validity of the concept Therefore, if the scale meets the Cronbach's Alpha standard, the content relevance of the variables must also be considered to ensure the scale's overall validity and reliability.
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502 Bad GatewayUnable to reach the origin service The service may be down or it may not be responding to traffic from cloudflared
502 Bad GatewayUnable to reach the origin service The service may be down or it may not be responding to traffic from cloudflared
502 Bad GatewayUnable to reach the origin service The service may be down or it may not be responding to traffic from cloudflared
Factor extraction criteria in factor analysis primarily include the Eigenvalue index, which measures the amount of variation explained by each factor, and the Cumulative index, indicating the total variance explained by all selected factors Factors with an Eigenvalue less than a specified threshold are typically disregarded to ensure the robustness of the analysis and to retain only meaningful factors The cumulative percentage elucidates the overall explanatory power of the chosen factors, helping researchers determine the adequacy of the factor model Utilizing these criteria optimizes factor selection, enhances the interpretability of results, and aligns with best practices in statistical analysis for reliable insights.
In this study, factors are extracted only when the Eigenvalue is greater than or equal to 1, and the total variance explained reaches at least 50%, ensuring meaningful factor representation The Eigenvalues and the proportion of variance extracted are influenced by the choice of extraction method and rotation technique Specifically, this analysis employs the Principal Axis Factoring method combined with Promax rotation to facilitate accurate factor interpretation Standard factor loadings are used to measure the correlation between variables and underlying factors, helping to assess the significance of each factor.
In factor analysis, factor loadings greater than 0.3 are considered minimal, those exceeding 0.4 are deemed important, and loadings above 0.5 are regarded as significant Additionally, it is recommended that for factor loadings above 0.3, the sample size should be at least 350 participants If the sample size is approximately 100, a factor loading of over 0.55 is advisable For smaller sample sizes around 50, the factor loading threshold should be adjusted accordingly to ensure reliable results, emphasizing the importance of adequate sampling in factor analysis for valid outcomes.
100 factor loading must be > 0.75 In this study with 470 samples, the Factor loading level was 0.3
When evaluating observed variables, it is essential to consider their content value alongside statistical metrics like Cronbach's Alpha If a variable exhibits a low factor loading or is associated with multiple factors, its statistical contribution may be minimal; however, if it significantly contributes to the conceptual understanding of the measured construct, it should not be discarded Therefore, the decision to remove an observed variable should balance both its statistical significance and its content relevance to ensure the integrity of the measurement model.
Test model and research hypothesis
This study utilizes the SEM (Structural Equation Modeling) linear structural model analysis to test the research framework, leveraging its widespread use in behavioral sciences SEM effectively combines factor analysis with regression and path analysis, enabling precise measurement of the impact relationships among research variables Its strength lies in detecting and quantifying the direct and indirect effects within complex models, making it a powerful tool for understanding variable interactions.
This thesis examines the mediating role of information perception in user-reviewed product videos on students' purchase intention The SEM structure model was used to examine these correlations
Chapter 3 presented the research process from which to conduct research and evaluate
This study tests six research hypotheses to examine their influence, utilizing a two-step approach that includes both qualitative and quantitative research methods Initially, a preliminary study was conducted to develop a validated survey scale, followed by an official survey targeting customers residing and working in Ho Chi Minh City The research methodology, detailed in Chapter 3, encompasses data analysis techniques, processing procedures, coefficient calculations, and the standards used to evaluate model conformity, ensuring rigorous and reliable results.
RESEARCH RESULTS AND DISCUSSION
The results of the sample descriptive statistics are presented in Table 4.1
Table 4-1 - Descriptive statistics of the research sample according to the categories
Source: Calculation results from SPSS software
According to the results of Table 4.1, out of 251 people surveyed, the male gender is
The demographic breakdown shows that females represent the majority with 174 individuals, accounting for 69.3%, while males total 67 people, making up 26.7% The remaining 10 individuals, or 4%, identify as other genders Age-wise, most participants are between 18 and 22 years old, with 136 people (54.2%), followed by those aged 22 to 26 years at 74 individuals (29.5%) Younger participants under 18 number 6 (2.4%), and the 26 to 30 age group includes 27 people (10.8%) Data for participants over 30 is not specified.
Among the surveyed population, 40-year-olds make up 8 individuals, representing 3.2% of the total Income levels vary, with 93 people earning under 5 million VND, accounting for 37.1% Those earning between 5 to 10 million VND number 63, representing 25.1%, while 71 individuals earn from 10 to 20 million VND, making up 28.3% Additionally, 24 people have an income exceeding 20 million VND, accounting for 9.6% This data provides insight into the income distribution and age demographics within the community.
Source: Calculation results from SPSS software
The correlation coefficient matrix shows the individual correlation between the pairs of variables in the model The results show that the independent variables in the model IR,
Intrinsic rewards (IR), along with ER, IS, PO, FR, PR, SR, and TR, show a statistically significant positive correlation with customers' intention (Int) to purchase paid music services at the 1% significance level These findings indicate that intrinsic rewards play a crucial role in increasing consumers’ intention to buy paid music subscriptions Incorporating these factors into marketing strategies can effectively enhance customer engagement and drive paid music service subscriptions.
The study first assessed the reliability of the scale components using Cronbach's Alpha, ensuring internal consistency Following this, Exploratory Factor Analysis (EFA) was conducted to identify the underlying factors that best represent the observed variables This analytical step helps to validate the construct structure of the scale and enhances the overall robustness of the measurement tool.
42 in the scales The factors represent 36 observed variables obtained from Exploratory Factor Analysis The EFA analysis was performed through the following tests:
Table 4-3 - Rotated Component Matrix of independent variables
Source: Calculation results from SPSS software
The analysis indicates that the KMO coefficient of 0.874 confirms suitability for EFA, as it falls within the acceptable range of 0.5 to 1 Additionally, Bartlett's test results show a significance level below 0.05, indicating a strong linear correlation among observed variables and the underlying factors The factor rotation matrix reveals the relationships between factors and observed variables, with all variables displaying factor loadings above 0.55, thereby meeting the criteria for significant factor loadings.
Table 4-4 - KMO and Bartlett’s test of dependent variable
Source: Calculation results from SPSS software
The KMO coefficient of 0.874 indicates excellent sampling adequacy, satisfying the criterion of 0.5 < KMO < 1 and confirming that factor analysis is suitable for the data Additionally, Bartlett's test results, with a significance level (Sig) less than 0.05, demonstrate that the observed variables are significantly correlated with the underlying factors, validating the use of Exploratory Factor Analysis (EFA) for this dataset.
This study employed structural equation modeling (SEM) using the AMOS program to evaluate the validity of the model and the interactions among constructs The model demonstrated excellent fit, with key statistics including a Chi-square of 1504.347 (df = 823, p < 0.001), a Chi-square/df ratio of 1.828, Comparative Fit Index (CFI) of 0.956, Tucker-Lewis Index (TLI) of 0.952, Standardized Root Mean Square Residual (SRMR) of 0.058, and Root Mean Square Error of Approximation (RMSEA) of 0.051 The Normed Chi-square (X2/df) value of 1.828 indicates that the model effectively represents the data, confirming its robustness and validity.
Chapter 4 presents the empirical findings on the key factors influencing customers' intention to purchase online music services in Ho Chi Minh City The study was based on a survey conducted at Banking University of Ho Chi Minh City in November, providing valuable insights into consumer behavior in the digital music market.
Between November 16, 2021, and November 22, 2022, survey questionnaires were distributed both directly and indirectly via email A total of 300 questionnaires were sent out to participants, with invalid responses subsequently removed Ultimately, the valid sample size for analysis comprised 251 observations, providing a robust dataset for research insights.
The study begins with descriptive statistics analyzing the research sample’s gender, age, and major, providing a clear understanding of the demographic profile The empirical findings validate eight hypotheses related to factors influencing online music purchasing intentions in Ho Chi Minh City Specifically, six groups of factors—Intrinsic Rewards, Extrinsic Rewards, Identity Salience, Psychological Ownership, Financial Risk, Time Risk, Product Risk, and Security Risk—significantly impact consumer behavior These results confirm that the hypotheses are grounded in accurate data and sound conclusions, offering valuable insights for understanding online music service preferences.
CONCLUSION
Conclusion
The results of the measurement models show that, after having been added and adjusted, the scales have reached their reliability and allowable values This result has the following meanings:
This study advances the measurement of key constructs such as Intrinsic Rewards, Extrinsic Rewards, Identity Salience, Psychological Ownership, and various risk factors (Finance, Time, Product, Security) along with purchase intention by developing a comprehensive scale system tailored for the Vietnamese market This new scale system provides valuable tools for both academic and applied researchers studying consumer behavior in Vietnam and globally, facilitating more accurate and comparable data collection Additionally, it offers a foundation for creating standardized measurement scales in multinational consumer behavior research, addressing a critical gap in international marketing studies—particularly in developing countries—where the absence of baseline scales hinders the establishment of equivalent measurement systems (Craig & Douglas, 2000).
Measuring a latent concept with multiple observed variables can enhance the validity and reliability of the measurement, although it does not guarantee correctness for every study The design should accurately reflect the specific observed variables used, which can be adjusted and supplemented to suit different markets and service industries This customization is essential because each service has unique properties that influence measurement strategies.
Next, the results of the measurement model in this study contribute to stimulating researchers in the field of behavioral science in general and marketing in particular in
Ensuring the validity and reliability of measurement scales is essential when conducting research, as inaccurate assessments can compromise the credibility of the results Proper evaluation of these scales guarantees accurate data collection and meaningful insights Neglecting this step may lead to questionable findings, requiring researchers to reconsider and validate their results Therefore, thorough validation of measurement tools is a crucial prerequisite for producing trustworthy and impactful research outcomes.
The test results show the suitability of the theoretical model with market information as well as the acceptance of the hypothesis proposed in this study
This study examines the factors influencing customers' intention to purchase online music services in Ho Chi Minh City from a consumer psychology perspective It emphasizes understanding individual consumer motivations and preferences rather than focusing solely on company-driven factors The research highlights the importance of psychological drivers in shaping online music purchasing decisions, offering valuable insights for marketers targeting the Ho Chi Minh market.
Implication
The study reveals that the IS (Identity Salience) factor has the most significant impact on customers' online music service purchase intentions, making it a crucial focus for businesses To leverage this insight, companies should promote connections with social networking platforms to build music listening communities where users can enjoy music and interact actively Additionally, recommending personalized songs can enhance customers’ sense of uniqueness and self-expression, strengthening their emotional connection to the service Implementing programs that summarize individual music tastes can help customers better understand their personalities through their musical preferences, increasing engagement and loyalty.
The PO (ownership) factor significantly influences customers' intention to purchase online music services, highlighting the importance of fostering a strong ownership mindset To enhance customer engagement and loyalty, businesses should empower users by allowing them to enjoy their purchased songs according to personal preferences and offer options to donate or share their music with others Additionally, providing features that enable customers to create, customize, and share playlists encourages community interaction Companies can also enhance the user experience by facilitating communication between customers, such as enabling commenting, reacting to playlists, and fostering social interactions within the platform.
Understanding all aspects of a product helps customers feel a special connection, fostering a sense of ownership and loyalty Businesses can enhance this bond by creating loyalty programs that offer exclusive discounts, strengthening customer relationships and encouraging repeat engagement.
To enhance customer satisfaction and boost revenue, businesses should focus on improving key service features such as sound quality, user-friendly application interfaces, and advanced song playback options By optimizing these aspects, companies can provide a superior user experience, encouraging customers to become more accustomed to the platform and increasing their willingness to make purchases.
RESEARCH LIMITATIONS
Firstly, due to limited time and capacity, this study can only be conducted in Ho Chi Minh City
Secondly, this study only considers 8 influencing factors, but there are many other factors that can affect customers' intention to buy online music services
Third, when responding to a survey, respondents may answer honestly or lack interest, leading to a big impact on the research results.
FURTHER RESEARCH DIRECTIONS
First, the next study will expand the scope of the study to the whole of Vietnam and increase the sample size for broader research
Second, further research should expand the research model in a new direction and add new factors to get more comprehensive factors
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IR Phần thưởng nội tại Intrinsic Rewards
Tôi sử dụng dịch vụ phát nhạc trực tuyến MSS vì tôi thấy nó thú vị
I used Music Streaming Services (MSS) because I found it enjoyable
Tôi sử dụng MSS vì tôi thấy nó rất dễ chịu
I used Music Streaming Services (MSS) because Music Streaming Services (MSS) is pleasant
Tôi thấy hạnh phúc khi sử dụng MSS
I used Music Streaming Services (MSS) because Music Streaming Services (MSS) makes me happy
Tôi thấy hào hứng khi sử dụng MSS
I used Music Streaming Services (MSS) because Music Streaming Services (MSS) is exciting
ER Phần thưởng ngoại sinh Extrinsic Rewards
ER1 Chất lượng âm thanh là lý do khiến tôi sử dụng MSS
Sound quality is the reason I used MSS
ER2 Tính năng tối ưu là lý do khiến tôi sử dụng MSS
Ultimate features are the reason why I used MSS
ER3 Tôi sử dụng MSS vì tôi muốn tiết kiệm thời gian
I used MSS because I wanted to save time
Tôi có thể thu thập thông tin âm nhạc dễ dàng hơn thông qua các dịch vụ phát nhạc trực tuyến có trả phí
I can acquire music information more easily through paid music streaming services
SC Chi phí chuyển đổi Switching Costs
Tôi sẽ mất rất nhiều thời gian để chuyển từ MSS trả phí sang MSS miễn phí
It would take me a lot of time to switch from paid MSS to free MSS
Tôi sẽ tốn rất nhiều tiền để chuyển từ MSS trả phí sang MSS miễn phí
It would cost me a lot of money to switch from paid MSS to free MSS
Tôi sẽ mất rất nhiều công sức để chuyển từ MSS trả phí sang MSS miễn phí
It would take me a lot of effort to switch from paid MSS to free MSS
Tôi sẽ cảm thấy không chắc chắn nếu tôi phải chọn MSS miễn phí thay vì MSS trả phí
I will feel uncertain if I have to choose free MSS instead of paid MSS
FR Rủi ro tài chính Finance Risk
FR1 Tôi có xu hướng chi tiêu quá mức cho dịch vụ nghe nhạc có trả phí
I tend to over spend for paid music Streaming Services
FR2 Tôi có thể bị tính phí quá cao cho dịch vụ nghe nhạc có trả phí
I might get overcharged for paid music Streaming Services
Dịch vụ có thể không xứng đáng với số tiền tôi đã bỏ ra mua
Paid music Streaming Services may not be worth the money I spent
TR Rủi ro thời gian Time Risk
TR1 MSS trả phí có thể làm mất thời gian của tôi
Paid MSS can waste my time
TR2 Tôi khó tìm một MSS trả phí phù hợp với mình
I have a hard time finding paid MSS that works for me
PR Rủi ro sản phẩm Product Risk
Tôi không tin tưởng công ty cung cấp dịch vụ nghe nhạc trực tuyến có trả phí
I do not trust the online company providing Paid MSS
Tôi không thể tìm thấy sản phẩm nghe nhạc có trả phí mà tôi mong muốn
I am unable to find the desired product of Paid MSS
Tôi có thể không nhận được chất lượng đúng với số tiền mà tôi bỏ ra cho dịch vụ nghe nhạc có trả phí
I might not get the right quality for my money for Paid MSS
SR Rủi ro bảo mật Security Risk
Tôi cảm thấy rằng thông tin tài khoản thanh toán ứng dụng MSS trả phí của tôi không được bảo mật
I feel that my Paid MSS account information is insecure
SR2 Trang web có thể không an toàn The website may be insecure
SR3 Các công ty cung cấp MSS trả phí có thể tiết lộ thông tin của tôi
Companies that provide Paid MSS may disclose my information
Thông tin về công ty cung cấp MSS trả phí có thể không đủ
Information about the company providing the Paid MSS may not be sufficient
PO Tâm lý sở hữu Psychological ownership
Tôi cảm thấy rằng MSS trả tiền là của tôi
I sense that paid MSS is MINE
Tôi nhận thấy mức độ sở hữu rất cao cho các dịch vụ nghe nhạc trả phí
I feel a very high degree of personal ownership for paid MSS
Tôi nhận thấy mức độ sở hữu rất cao cho dịch vụ nghe nhạc mà tôi mua
I feel a very high degree of personal ownership for the paid MSS that I bought
Dịch vụ nghe nhạc trả phí mà tôi đã mua là của tôi
The paid MSS I bought is MINE
IS Khả năng nhận dạng Identity salience
IS1 Sử dụng MSS trả phí là một phần quan trọng của tôi
Using paid MSS is an important part of who I am
IS2 Việc sử dụng MSS trả phí được coi là có uy tín trong cộng đồng của tôi
It is considered prestigious in my community to use paid MSS
Sử dụng MSS trả phí cải thiện uy tín của tôi đối với những người quen biết trên mạng xã hội
Using paid MSS improves my credibility among social acquaintances
IS4 Mọi người trong cộng đồng của tôi tự hào khi sử dụng MSS trả phí
People in my community are proud to use paid MSS
SV Giá trị xã hội Social Value
Tôi đánh giá cao các dịch vụ phát trực tuyến nhạc có trả phí vì chúng nâng cao vị thế ngang hàng của tôi
I value paid music streaming services because they enhance my peer status
Tôi đánh giá cao việc sử dụng các dịch vụ phát nhạc trực tuyến có trả phí vì chúng giúp tăng kết nối của tôi trên mạng xã hội
I value paid music streaming services because they help increase my connections on social media
Tôi đánh giá cao việc sử dụng các dịch vụ phát trực tuyến âm nhạc có trả phí vì chúng mang lại trải nghiệm nghe nhạc chất lượng cao, không quảng cáo và sở hữu nhiều bài hát yêu thích Các dịch vụ này ngày càng phổ biến và là lựa chọn hàng đầu để thỏa mãn đam mê âm nhạc của người dùng Việc đầu tư vào dịch vụ phát trực tuyến trả phí giúp tôi tận hưởng nhạc một cách trọn vẹn và thuận tiện hơn bao giờ hết.
I value paid music streaming services because they are popular among my peers
Tôi đánh giá cao các dịch vụ phát trực tuyến nhạc có trả phí vì chúng cải thiện hình ảnh của tôi giữa bạn bè và gia đình
I value paid music streaming services because they improve my image among my friends and family
Int Ý định tiếp tục sử dụng dịch vụ nghe nhạc có trả phí
Usage Continuance Intention toward paid MSS
Int1 Tôi định tiếp tục sử dụng MSS có trả phí
I intend to continue to use paid MSS
Int2 Tôi nghĩ rằng tôi sẽ sử dụng MSS có trả phí trong vòng sáu tháng
I think I am going to use paid MSS within six months
Int3 Tôi nghĩ rằng tôi sẽ sử dụng MSS có trả phí trong vòng một năm
I think I am going to use paid MSS within one year
Phạm Lê Quỳnh Hoa, sinh viên ngành Quản trị kinh doanh hệ chất lượng cao tại Đại học Ngân hàng TP.HCM, đang thực hiện khóa luận tốt nghiệp với đề tài nổi bật.
Nghiên cứu này nhằm khảo sát các nhân tố ảnh hưởng đến quyết định mua dịch vụ nghe nhạc có phí ở khách hàng tại TP Hồ Chí Minh Nội dung câu hỏi tập trung đánh giá và phản hồi về việc sử dụng các ứng dụng nghe nhạc trực tuyến, nhằm hiểu rõ các yếu tố quyết định xu hướng tiêu dùng của người dùng Chúng tôi rất mong nhận được ý kiến đóng góp chân thành từ anh chị để nâng cao chất lượng nghiên cứu và phục vụ tốt hơn cho yêu cầu của khách hàng.
Xin lưu ý rằng mọi ý kiến đóng góp chân thành đều rất quý giá và đóng vai trò quan trọng trong quá trình nghiên cứu của chúng tôi, không có câu trả lời đúng hay sai Mọi thông tin thu thập được sẽ được bảo mật tuyệt đối và chỉ sử dụng để phục vụ mục đích nghiên cứu, đảm bảo an toàn và riêng tư cho người tham gia.
PHẦN I: THÔNG TIN CÁ NHÂN
PHẦN II: NỘI DUNG CHÍNH
Dưới đây là bảng khảo sát mức độ tác động của các nhân tố đến quyết định mua và sử dụng dịch vụ ứng dụng nghe nhạc trực tuyến:
Anh chị có đang sử dụng nền tảng nghe nhạc trực tuyến nào không?
Anh chị hiện có trả tiền cho bất kỳ dịch vụ phát trực tuyến nhạc nào không?
Nếu có trả phí cho các dịch vụ nghe nhạc, thì đó là ứng dụng nào?
Nếu anh chị không trả phí cho các dịch vụ nghe nhạc, phiền anh chị cho biết lý do?
IR Phần thưởng nội tại 1 2 3 4 5 6 7
IR1 Tôi sử dụng dịch vụ phát nhạc trực tuyến MSS vì tôi thấy nó thú vị � � � � � � �
IR2 Tôi sử dụng MSS vì tôi thấy nó rất dễ chịu � � � � � � �
IR3 Tôi thấy hạnh phúc khi sử dụng
IR4 Tôi thấy hào hứng khi sử dụng
ER Phần thưởng ngoại sinh 1 2 3 4 5 6 7
ER1 Chất lượng âm thanh là lý do khiến � � � � � � �
ER2 Tính năng tối ưu là lý do khiến tôi sử dụng MSS � � � � � � �
ER3 Tôi sử dụng MSS vì tôi muốn tiết kiệm thời gian � � � � � � �
Tôi có thể thu thập thông tin âm nhạc dễ dàng hơn thông qua các dịch vụ phát nhạc trực tuyến có trả phí
SC Chi phí chuyển đổi 1 2 3 4 5 6 7
Tôi sẽ mất rất nhiều thời gian để chuyển từ MSS trả phí sang MSS miễn phí
SC2 Tôi sẽ tốn rất nhiều tiền để chuyển từ MSS trả phí sang MSS miễn phí � � � � � � �
Tôi sẽ mất rất nhiều công sức để chuyển từ MSS trả phí sang MSS miễn phí
Tôi sẽ cảm thấy không chắc chắn nếu tôi phải chọn MSS miễn phí thay vì MSS trả phí
FR Rủi ro tài chính 1 2 3 4 5 6 7
FR1 Tôi có xu hướng chi tiêu quá mức cho dịch vụ nghe nhạc có trả phí � � � � � � �
FR2 Tôi có thể bị tính phí quá cao cho dịch vụ nghe nhạc có trả phí � � � � � � �
FR3 Dịch vụ có thể không xứng đáng với số tiền tôi đã bỏ ra mua � � � � � � �
TR Rủi ro thời gian 1 2 3 4 5 6 7
TR1 MSS trả phí có thể làm mất thời gian của tôi � � � � � � �
TR2 Tôi khó tìm một MSS trả phí phù hợp với mình � � � � � � �
PR Rủi ro sản phẩm 1 2 3 4 5 6 7
Tôi không tin tưởng công ty cung cấp dịch vụ nghe nhạc trực tuyến có trả phí
Tôi không thể tìm thấy sản phẩm nghe nhạc có trả phí mà tôi mong muốn
Tôi có thể không nhận được chất lượng đúng với số tiền mà tôi bỏ ra cho dịch vụ nghe nhạc có trả phí
SR Rủi ro bảo mật 1 2 3 4 5 6 7
Tôi cảm thấy rằng thông tin tài khoản thanh toán ứng dụng MSS trả phí của tôi không được bảo mật
SR2 Trang web có thể không an toàn � � � � � � �
SR3 Các công ty cung cấp MSS trả phí có thể tiết lộ thông tin của tôi � � � � � � �
SR4 Thông tin về công ty cung cấp MSS trả phí có thể không đủ � � � � � � �
PO Tâm lý sở hữu 1 2 3 4 5 6 7
Tôi cảm thấy rằng MSS trả tiền là của tôi � � � � � � �
Tôi nhận thấy mức độ sở hữu rất � � � � � � �
62 cao cho các dịch vụ nghe nhạc trả phí
Tôi nhận thấy mức độ sở hữu rất cao cho dịch vụ nghe nhạc mà tôi mua
Dịch vụ nghe nhạc trả phí mà tôi đã mua là của tôi � � � � � � �
IS Nổi bật nhận dạng 1 2 3 4 5 6 7
IS1 Sử dụng MSS trả phí là một phần quan trọng của tôi � � � � � � �
IS2 Việc sử dụng MSS trả phí được coi là có uy tín trong cộng đồng của tôi � � � � � � �
Sử dụng MSS trả phí cải thiện uy tín của tôi đối với những người quen biết trên mạng xã hội
IS4 Mọi người trong cộng đồng của tôi tự hào khi sử dụng MSS trả phí � � � � � � �
SV Giá trị xã hội 1 2 3 4 5 6 7
Tôi đánh giá cao các dịch vụ phát trực tuyến nhạc có trả phí vì chúng nâng cao vị thế ngang hàng của tôi
Tôi đánh giá cao việc sử dụng các dịch vụ phát nhạc trực tuyến có trả phí vì chúng giúp tăng kết nối của tôi trên mạng xã hội
Tôi đánh giá cao việc sử dụng các dịch vụ phát trực tuyến âm nhạc có trả phí vì chúng mang lại trải nghiệm nghe nhạc chất lượng cao và đáng tin cậy Các dịch vụ này ngày càng phổ biến trong danh sách các nền tảng phát trực tuyến của tôi, giúp tôi dễ dàng khám phá và thưởng thức các bài hát yêu thích một cách tiện lợi và ổn định.